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一个肺高血压向算法,以改善转诊到右心脏导管:一个机器学习方法

Paola Argiento¹, Anna D'Agostino², Rossana Castaldo²

¹Department of Cardiology, University "L. Vanvitelli"-Monaldi Hospital, Naples, Italy.

Computational and structural biotechnology journal

|December 17, 2024

概括

这项研究开发了一种机器学习算法,使用患者数据来预测肺高血压 (PH),旨在改善早期检测和导入侵性右心导管治疗 (RHC) 的转诊. 该模型在识别PH方面表现出很好的准确性,可能简化临床查.

科学领域:

心脏病学心脏病学
肺部病理学肺部病理学
医疗信息学医疗信息学

背景情况:

肺高血压 (PH) 是一种复杂的疾病,与呼吸道和心血管疾病有关.
目前的诊断依赖于侵入性右心导管注射 (RHC),这给例行查带来了挑战.
开发非侵入性预测工具对于早期PH检测至关重要.

研究的目的:

开发和验证用于预测侵入性PH的机器学习 (ML) 算法.
利用历史记录,非侵入性和临床数据进行PH风险分层.
提高PH诊断和患者转诊途径的效率.

主要方法:

在2017年10月至2020年10月期间,招募了226名疑似PH患者接受RHC.
收集了患者的特征,病史数据,胸前心声图 (TTE) 和螺旋计结果.
训练并验证了一种弹性网规则化通用线性模型,使用来自51个收集数据点的8个显著变量.

主要成果:

确定了12个变量,可以显著区分PH阳性和PH阴性患者.
开发了一个风险评分模型,曲线下的面积 (AUC) 为83%,整体准确率为74%.
在PH和没有PH的患者之间表现出非常好的歧视.

结论:

针对PH的ML模型可以增强肺高血压的常规查.
促进PH的早期识别可以导致更好的RHC转诊.
开发的算法在简化PH诊断方面表现有前途.

关键词:

早期诊断早期诊断早期诊断机器学习机器学习肺高血压是一种肺高血压.

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